In
this study, the estimation of the low temperature performance of modified
asphalt mixtures is investigated by using multi-layer perceptrons (MLP) which
is one of the artificial neural networks (ANNs) techniques and general linear
model (GLM). The fastest MLP training algorithm, that is the
Levenberg-Marquardt algorithm, is used for optimization of the network weights.
The ANN test results are compared to GLM results. GLM has, historically, been
used to model the low temperature performance (fracture temperature and fracture
strength) of asphalt pavements. The data used in the ANN model and GLM are
arranged in a format of four input parameters that cover additive type, asphalt
binder content, aging level and air void content, and output parameters which
are the fracture temperature and the fracture strength. Based on the
comparisons, it is found that the ANN generally gives better fracture
temperature and fracture strength estimates than the GLM technique.

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